
Ekkono

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About
Ekkono is an Edge AI platform designed for deployment on resource-constrained devices. It offers a small footprint, hardware and sensor agnosticism, and model life-cycle management (MLCM). Key features include individual unit monitoring, adaptive automation, and the ability to run AI on various devices. Ekkono supports real-time on-device training and addresses tasks such as condition monitoring, condition-based maintenance, performance optimization, and sustainable product development. It's designed to work with sensor data, not just images, offering features like incremental learning, real-time preprocessing, and anomaly detection, lacking in other tools. The platform offers a Software Development Kit (SDK) to support the entire workflow from application engineer to embedded software engineer.
Platform
Task
Features
• performance optimization
• condition monitoring
• edge ai
• sustainable products
• condition-based maintenance
• ai on everything
• adaptive automation
• individual unit monitoring
FAQs
How does Ekkono handle model verification and ensure correct learning?
Ekkono supports real-time, on-device training and adaptation for tasks like analyzing sensor data (temperature, pressure, power consumption), making it ideal for deploying efficient, adaptive models on constrained devices.
What is meant by Ekkono's "small footprint"?
Ekkono's small footprint allows it to run on devices with minimal CPU and memory, enabling advanced machine learning even on highly constrained hardware like Arduino Pro Micro or Commodore 64.
How is Ekkono different from TensorFlow Lite/Micro?
TensorFlow Lite/Micro focuses on deep learning tasks like image classification, lacking capabilities like incremental learning, real-time preprocessing, and anomaly detection crucial for many edge ML use cases involving sensor data. Ekkono is purpose-built for these edge ML tasks.
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